Nowadays, capability of performing reliable human identification and recognition has become a crucial requirement in many applications, such as forensics, airport custom check, and bank securities. Current state-of-the-art techniques for human identification rely on discriminative physiological and behavioral characteristics of human, known as biometrics.
Biometric recognition refers to an automated recognition of individuals based on their human biological and behavioral characteristics. Some well-known biometrics for human recognition may include fingerprint, face, iris, and voice. Since biometric information is inherent and distinctive to an individual, biometric traits are widely used in surveillance systems for human identification. Moreover, due to difficulty for biometrics counterfeit, techniques based on biometrics have clear-cut advantages over traditional security methods such as passwords and signatures in countering the growing security threats and in facilitating personalization and convenience. Even though current biometrics systems can be applied in some environments, all of them require special devices that capture human biometric traits in an extremely line-of-sight (LOS) environment. A LOS environment means there is a direct LOS path between the device and the human (or other test subjects). For example, to collect a fingerprint, a person usually puts a finger on top of the fingerprint scanner, where there is a direct straight path of light between the scanner (capturing device) and the finger (test subject). In contrast, in a non-line-of-sight (NLOS) environment, there are some blockages, e.g. walls, between the device and the test subject, such that no light can directly pass through the straight path between the device and the test subject.
Some researchers studied a relationship between the electromagnetic (EM) absorption of human bodies and the human physical characteristics in the carrier frequency range of 1 to 15 GHz, in which the body's surface area is found to have a dominant effect on absorption. Moreover, the interaction of EM waves with biological tissue has been studied and the dielectric properties of biological tissues have been measured. The wireless propagation around the human body highly depends on the physical characteristic (e.g., height and mass), the total body water volume, the skin condition and other biological tissues. The human-affected wireless signal under attenuations and alterations, containing the identity information, may be defined as human radio biometrics or human radio biometric information. Considering the combination of all the physical characteristics and other biological features that affect the propagation of EM waves around the human body and how variable those features can be among different individuals, the chance for two human individuals to have an identical combination is significantly small, no matter how similar those features are. Even if two persons have the same height, weight, clothing and gender, other inherent biological characteristics may be different, resulting in different wireless propagation patterns round the human body. Taking the Deoxyribonucleic acid (DNA) sequence as an example, even though all humans are 99.5% similar to any other humans, no two human individuals are genetically identical, which is the key to techniques such as genetic fingerprinting. Since the probability for two individuals to have exactly the same physical and biological characteristics is extremely small, multipath profiles after human interferences are therefore different among different persons. Consequently, human radio biometrics, which record how the wireless signal interacts with a human body, are altered according to individuals' biological and physical characteristics and can be viewed as unique among different individuals. One example is that face recognition has been implemented for many years to distinguish from and recognize different people, thanks to the fact that different individuals have different facial features. Human radio biometrics, which record how radio frequency (RF) signals respond to the entire body of a human including the face, should contain more information than a face, and thus become more distinct among humans.
In the recent past, a number of attempts have been made to detect and recognize indoor human activities through wireless indoor sensing. Systems have been built to detect indoor human motions based on the variations of channel state information (CSI). They made use of: first two largest eigenvalues of the CSI correlation matrix; standard deviation of the CSI samples from a 3×3 MIMO system to detect human activities such as falling; the received signal strength (RSS) as an indicator for the fluctuation of the wireless channel quality; wireless signal for tracking and recording vital signals. Some system has been disclosed to track human breathing and heartbeat rate using off-the-shelf Wi-Fi signals. A Vital-Radio system was disclosed to monitor vital signs using radar technique to separate different reflections. On the other hand, the recognition of gestures and small hand motions has been implemented using wireless signals. By sending a specially designed frequency modulated carrier wave (FMCW) which sweeps over different carrier frequencies, a new radar-based system was disclosed to keep track of the different time-of-flights (ToFs) of the reflected signals. However, as focusing on differentiating between different human movements, e.g., standing, walking, falling down and small gestures, none of the existing works have addressed the problem of distinguishing one individual from others, who hold the same posture and stand at the same location, by only using Wi-Fi signals in a through-the-wall setting. Recently, a RF-Capture system was presented to image human body contour through the wall. Owing to the distinctiveness of silhouettes, it can differentiate between different individuals by applying image processing and machine learning techniques to the captured human figures. However, to get a high-resolution ToF profile, it requires special devices that can scan over 1 GHz spectrum. Moreover, the computational complexity introduced by the necessary image processing and machine learning algorithms is high.
Therefore, there is a need to build a human identification system to solve the above-mentioned problems and to avoid the above-mentioned drawbacks.